nyu-mll/glue
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How to use gokulsrinivasagan/bert_tiny_lda_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_tiny_lda_sst2") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_lda_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_tiny_lda_sst2", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_tiny_lda on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4256 | 1.0 | 264 | 0.4628 | 0.8062 |
| 0.2595 | 2.0 | 528 | 0.4648 | 0.8108 |
| 0.2022 | 3.0 | 792 | 0.4879 | 0.8073 |
| 0.1673 | 4.0 | 1056 | 0.5045 | 0.8211 |
| 0.1402 | 5.0 | 1320 | 0.5442 | 0.8108 |
| 0.123 | 6.0 | 1584 | 0.6225 | 0.8005 |
Base model
gokulsrinivasagan/bert_tiny_lda